import datetime
import time
import numpy as np
import pymysql 
import pandas as pd
import requests
from sklearn.preprocessing import MinMaxScaler
from joblib import dump

class WeatherUtils(object):
    """
        天气类
    """
    def __init__(self):
        self.data_list = [
            '2024-07-01',
            '2024-08-01',
            '2024-09-01',
            '2024-10-01',
            '2024-11-01',
            '2024-12-01',
            
        ]
        
        self.url = 'http://v1.yiketianqi.com/api'
        
    def get_data(self):
        """
        获取天气数据
        """
        date_list = []
        for d in self.data_list:
            conf = {
                'appid': '45327191',
                'appsecret': '7rWMu5LX',
                'version': 'history',
                'year':d[:4],
                'month':d[5:7],
                'city': '南昌',
                
            }
            res = requests.get(self.url, params=conf)
            res_data = res.json()
            for i in res_data['data']:
                date_list.append({
                    'date':datetime.datetime.strptime(i['ymd'], '%Y-%m-%d'),
                    'bWendu':i['bWendu'],
                    'yWendu':i['yWendu'],
                    'tianqi':i['tianqi'],
                    'fengxiang':i['fengxiang'],
                    'fengli':i['fengli'],
                })
                
        df = pd.DataFrame(date_list)
        df.to_csv('timeing/weather.csv', index=False)


class MysqlUtils(object):

    def __init__(self) -> None:
        self.conn = pymysql.connect(
            host= '127.0.0.1',
            user= 'root',
            password= 'root',
            database= 'scenic1',
            port= 3306,          # 明确指定端口
            charset= 'utf8mb4'   # 添加字符集设置
        )
        self.weather_df = pd.read_csv('timeing/weather.csv')
        
    def is_holiday(self, date):
        """是否节假日判断
        """
        if date in ['2024-09-03', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2025-01-01', '2025-01-02', '2025-01-03']:
            return 1
        return 0
        
    def get_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT DATE(g.create_time) as date,   count(*) as count 
        FROM order_user_gate_rel g WHERE g.create_time BETWEEN '2024-07-01' and '2025-01-01' GROUP BY date
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)
        #合并天气数据
        self.weather_df['date'] = pd.to_datetime(self.weather_df['date'])
        df['date'] = pd.to_datetime(df['date'])
        df_pivot = pd.merge(df, self.weather_df, on='date')
        df_pivot.set_index('date', inplace=True)
        df_pivot['dow'] = df_pivot.index.dayofweek # 星期几（0-6）
        df_pivot['month'] = df_pivot.index.month # 月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        
        # # 对星期几和月份、天气和风力、风向进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month', 'tianqi', 'fengli','fengxiang'], dtype=int)
        
        # 对温度进行类型转换
        df_pivot['bWendu'] = df_pivot['bWendu'].str.replace('°', '').astype(int)
        df_pivot['yWendu'] = df_pivot['yWendu'].str.replace('°', '').astype(int)
        
        # # 归一化入园数
        scaler = MinMaxScaler()
        feature = df_pivot[['count']]
        df_pivot['count'] = scaler.fit_transform(feature)
        dump(scaler, 'timeing/scaler.joblib')
        #归一化天气
        weather_features = ['bWendu', 'yWendu']
        df_pivot[weather_features] = scaler.fit_transform(df_pivot[weather_features])
        dump(scaler, 'timeing/weather_scaler.joblib')
        
        df_pivot.to_csv('timeing/scenic_data.csv', index=False)
        
        
       
        
if __name__ == '__main__':
    # wu = WeatherUtils()
    # wu.get_data()
    mu = MysqlUtils()
    mu.get_data()